Conference paper Open Access

Multi-objective Container Deployment on Heterogeneous Clusters

Hu, Yang; de Laat, Cees; Zhao, Zhiming


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    <subfield code="a">&lt;p&gt;Operating system (OS) containers are becoming&lt;/p&gt;

&lt;p&gt;increasingly popular in cloud computing for improving productivity&lt;/p&gt;

&lt;p&gt;and code portability. However, existing deployment&lt;/p&gt;

&lt;p&gt;scheduling solutions mainly treat each container deployment&lt;/p&gt;

&lt;p&gt;as an independent request, and focus on the single aspect of&lt;/p&gt;

&lt;p&gt;resource utilization or load balancing, or work on homogeneous&lt;/p&gt;

&lt;p&gt;clusters. In this paper, we propose a new container deployment&lt;/p&gt;

&lt;p&gt;algorithm to satisfy multiple objectives on heterogeneous clusters.&lt;/p&gt;

&lt;p&gt;We analyze the deployment requirements of container-based&lt;/p&gt;

&lt;p&gt;infrastructure and formulate the deployment problem as a vector&lt;/p&gt;

&lt;p&gt;bin packing problem with heterogeneous bins. We focus on&lt;/p&gt;

&lt;p&gt;three objectives: multi-resource guarantee, load balancing, and&lt;/p&gt;

&lt;p&gt;dependency awareness. The goal of the proposed algorithm is&lt;/p&gt;

&lt;p&gt;to improve the tradeoff between load balancing and dependency&lt;/p&gt;

&lt;p&gt;awareness with multi-resource guarantees. Based on the algorithm,&lt;/p&gt;

&lt;p&gt;we implement a prototype scheduler to deploy containers&lt;/p&gt;

&lt;p&gt;on heterogeneous clusters. We evaluate our scheduler over a&lt;/p&gt;

&lt;p&gt;wide range of workload scenarios by simulation, which shows&lt;/p&gt;

&lt;p&gt;that our scheduler significantly outperforms existing schedulers&lt;/p&gt;

&lt;p&gt;of the container orchestration platforms.&lt;/p&gt;</subfield>
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